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5th World Robot Conference Symposium on Advanced robotics and automation, WRC SARA 2023

作者： Ma, Xudong Kuang, Ziao Li, Han Chen, Anqi Li, Changsheng Liu, Wenyong Wang, Junchen Hu, Yida David Gao, Li Su, Bai-Quan Beijing University of Posts and Tele-communications Medical Robotics Laboratory School of Automation Beijing100876 China Beijing University of Posts and Tele-communications School of Computer Beijing100876 China Beijing Institute of Technology School of Mechatronical Engineering Beijing100081 China Beihang University Key Laboratory for Biomechanics and Mechanobiology of Chinese Education Ministry Beijing Advanced Innovation Centre for Biomedical Engineering School of Biological Science and Medical Engineering Beijing100083 China Beihang University School of Mechanical Engineering and Automation Beijing100083 China Brigham and Women's Hospital Harvard Medical School BostonMA02115 United States Peking University School and Hospital of Stomatology National Clinical Research Center for Oral Diseases National Stomatological Center Department of Periodontology Beijing China

ISBN: (纸本)9798350307320

All of oral surgeries are accompanied by bleeding. Manual removal of blood is carried out by a nurse and dozens of repetitive blood suction operations are implemented by the nurse during a single oral surgery. In our previous work, a robot for removal of blood-water mixed liquid is designed. However, the robot requires an initial order to autonomous removal of blood mixed liquid. In this paper, a hand gesture control method for typical operations of the robot is developed. Gesture dataset are developed at first and a deep learning neural network is trained for detection of hand gesture. The gesture control procedures for four typical operations are provided in detail. In a group of experiments, it's demonstrated that an oral surgeon controls the robot's motion by presenting prescribed hand gesture. The proposed method could be an alternative manner for blood mixed liquid removal. © 2023 IEEE.

关键词： Blood

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Smart Insole: Stand-Alone Soft 3-Axis Force Sensing Array in a Shoe

Smart Insole: Stand-Alone Soft 3-Axis Force Sensing Array in...

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IEEE SENSORS

作者： Jian Gao Zihao Pu Ruixin Qiu Ying Li Xiulun Yin Kieran Morton Sadan Wani Justin Wyss Michael Steszyn Ryusuke Ishizaki Fumiya Hamatsu Takeshi Ohsato John D.W. Madden Department of Electrical & Computer Engineering Advanced Material Processing Engineering Laboratory University of British Columbia Vancouver Canada i-generator Portland USA Frontier Robotics Honda R&D Co. Ltd Saitama Japan

Foot plantar force monitoring has been explored using in-shoe soft insole measurements for application in diabetic foot and to assess footwear performance. Sensors are impractical for regular use due to bulky electronics and short battery life. They typically lack shear sensing. A soft flexible and custom configurable 3-axis capacitive sensing array is demonstrated through a customizable arrangement of 16-shear and normal force taxels completely imbedded in a shoe insole that wirelessly updates at 50 $\mathrm{n}_{2}$ . A patterned silicone dielectric mounted on a flex printed circuit board enables normal and shear sensitivities of 19.9 and 2.8 $\text{kPa}^{-1}$ . The 20 $\text{mW}$ power consumption includes transmission of data to a nearby mobile device, which is satisfied for more than 50 hours using a built-in 350 mAh LiPo battery. Operation in a shoe is demonstrated.

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Conceptual Design and Simulation of a BCF Swimming Soft Robotic Fish for Deep-Sea

Conceptual Design and Simulation of a BCF Swimming Soft Robo...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Yanyan Ding, Menglong Zhu, Shiqiang Liang, Yiming Bai, Yunhe Xie, Keren Wan, Minhong Gu, Jason Li, Tiefeng Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H4R2 Canada Center for X-mechanics Zhejiang University Hangzhou310027 China Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Department of Engineering Mechanics Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665481090

This work provides a design of a deep-sea soft robotic fish propelled by the body and/or caudal fin (BCF) mode. The robotic fish consists of a bionic fish head and a BCF propulsion body. To enhance the swimming performance of the first soft robot fish in the deep sea, the design combines the advantages of high efficiency in BCF propulsion with the large deformation angle of dielectric elastomers (DE) flexible joints. The connection of the bionic fish head and BCF is cast in one piece of silicone to achieve deep-sea pressure self-adaptation of the soft robotic fish. The swimming performance was simulated using the CFD method, and the BCF propulsion behaviors were analyzed to evaluate the design proposed. The design goal was verified through numerical calculation, and a further enhanced strategy for the thrust ability of the fish was presented as well. This work can provide a low-cost bionic soft robot fish solution for deep-sea exploration. © 2022 IEEE.

关键词： Conceptual design

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Task-Oriented Grasp Prediction with Visual-Language Inputs

Task-Oriented Grasp Prediction with Visual-Language Inputs

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chao Tang Dehao Huang Lingxiao Meng Weiyu Liu Hong Zhang Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Stanford University United States

To perform household tasks, assistive robots receive commands in the form of user language instructions for tool manipulation. The initial stage involves selecting the intended tool (i.e., object grounding) and grasping it in a task-oriented manner (i.e., task grounding). Nevertheless, prior researches on visual-language grasping (VLG) focus on object grounding, while disregarding the fine-grained impact of tasks on object grasping. Task-incompatible grasping of a tool will inevitably limit the success of subsequent manipulation steps. Motivated by this problem, this paper proposes GraspCLIP, which addresses the challenge of task grounding in addition to object grounding to enable task-oriented grasp prediction with visual-language inputs. Evaluation on a custom dataset demonstrates that GraspCLIP achieves superior performance over established baselines with object grounding only. The effectiveness of the proposed method is further validated on an assistive robotic arm for grasping previously unseen kitchen tools given the task specification. Our presentation video is available at: https://***/watch?v=e1wfYQPeAXU.

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FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration

arXiv

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arXiv 2023年

作者： Stathoulopoulos, Nikolaos Koval, Anton Agha-Mohammadi, Ali-Akbar Nikolakopoulos, George Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå971 87 Sweden Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots’ poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework’s main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations. Copyright © 2023, The Authors. All rights reserved.

关键词： Antennas

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Robust Event-Triggered Control of Nonlinear Systems Under a Global Sector-Bound Condition

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Guidance, Navigation and Control 2022年第2期2卷 59-79页

作者： Pengpeng Zhang Tengfei Liu Zhong-Ping Jiang Department of Control Science&Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

This paper studies an event-tniggered control problem for nonlinear systems subject to both external disturbancoes and dy namic *** is assumed that the system satisfies a global sector bound *** avold infnitely fast samplng,a novel eventriggred sampling mechanism is propoeed,which use8 not only the measuned system state but also an estimation of the inluence of the *** the propoeed design,the intersampling intervals an be lower bounded by a poeitive constant,and it is independent of botb external disturbances and dynamie ***,the doeedl loop event-tniggered system i proved to be input-torstate stable with repect to the extemal *** smalgain techmigues are;used for the stability analysis of the dloeeil-bop system.

关键词： Event-triggered control external disturbance dynamic uncertainty input-to-state stability

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MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection

MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Na...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Olaya Álvarez-Tuñón Hemanth Kanner Luiza Ribeiro Marnet Huy Xuan Pham Jonas le Fevre Sejersen Yury Brodskiy Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiRLab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark Automatic Control Group (RAT) Paderborn University Paderborn Germany

This paper presents MIMIR-UW, a multipurpose underwater synthetic dataset for SLAM, depth estimation, and object segmentation to bridge the gap between theory and application in underwater environments. MIMIR-UW integrates three camera sensors, inertial measurements, and ground truth for robot pose, image depth, and object segmentation. The underwater robot is deployed within a pipe exploration scenario, carrying artificial lights that create uneven lighting, in addition to natural artefacts such as reflections from natural light and backscattering effects. Four environments totalling eleven tracks are provided, with various difficulties regarding light conditions or dynamic elements. Two metrics for dataset evaluation are proposed, allowing MIMIR-UW to be compared with other datasets. State-of-art methods on SLAM, segmentation and depth estimation are deployed and benchmarked on MIMIR-UW. Moreover, the dataset's potential for sim-to-real transfer is demonstrated by leveraging the segmentation and depth estimation models trained on MIMIR-UW in a real pipeline inspection scenario. To the best of the authors' knowledge, this is the first underwater dataset targeted for such a variety of methods. The dataset is publicly available online. https://***/remaro-network/MIMIR-UW/

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Machine Vision Applications in robotics and automation

Machine Vision Applications in Robotics and Automation

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International Conference on Science Technology engineering and Management (ICONSTEM)

作者： Bharathi GP Kavitha P Laxman Baburao Abhang G. Rathinasabapathi Vipin Sharma T. Sathish Department of Computer and Communication Engineering Sri Sai Ram Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering R.M.K Engineering College Kavaraipettai Tamil Nadu India Department of Automation and Robotics Pravara Rural Engineering College Loni Maharashtra India Department of Mechanical Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Medi-Caps University Indore Madhya Pradesh Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The extensive incorporation of machine vision into the fields of robotics and automation in a variety of different ways. The various uses of machine vision and the revolutionary impact it has on the capabilities of robotic systems. This investigation is fueled by improvements in imaging technology and artificial intelligence. In the abstract, major features such as object detection, navigation, and human-robot interaction are discussed in depth. The abstract also demonstrates how machine vision algorithms contribute to enhanced precision, efficiency, and adaptability in automation processes. The purpose of thisis to shed light on the role that machine vision plays in revolutionising industrial automation, collaborative robotics, and autonomous systems by conducting an examination of real-world implementations and case studies. This all-encompassing investigation not only highlights the current status of machine vision applications but also forecasts the future trajectory of this technology, thereby establishing it as a foundational component for the development of robotics and automation in a variety of fields.

关键词： automation Service robots Navigation Machine vision Imaging Human-robot interaction Object detection

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Advances in artificial joint bio-friction and wear simulation assessment technology

Advances in artificial joint bio-friction and wear simulatio...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Yanyan Liang, Yiming Zhu, Shiqiang Ding, Menglong Bai, Yunhe Du, Haoyuan Gu, Jason Li, Tiefeng Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H4R2 Canada Center for X-mechanics Zhejiang University Hangzhou310027 China Zhejiang University Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Department of Engineering Mechanics Hangzhou310027 China

ISBN: (纸本)9781665481090

Natural joints have excellent low-friction properties that positively inspire the frictional wear properties of artificial joints. Replacing damaged biological joints with artificial ones can restore bio-function and reduce the pain of arthritic patients. Improvements in the frictional wear properties of artificial joints will effectively increase the life of the prosthesis, especially for young arthritic patients. This paper reviews the lubrication mechanism of natural joints before systematically introducing the development of lubrication and wear simulation techniques for artificial joints, offering references and insights for further studies of artificial joints and the optimization of tribological properties of commercial artificial joint prostheses. © 2022 IEEE.

关键词： Friction

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Learning Fine-Grained Alignment for Aerial Vision-Dialog Navigation 39

Learning Fine-Grained Alignment for Aerial Vision-Dialog Nav...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Su, Yifei An, Dong Chen, Kehan Yu, Weichen Ning, Baiyang Ling, Yonggen Huang, Yan Wang, Liang School of Artificial Intelligence University of Chinese Academy of Sciences China MAIS Institute of Automation of Chinese Academy of Sciences China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Electrical and Computer Engineering Department Carnegie Mellon University United States Robotics X Tencent Shenzhen China

ISBN: (纸本)157735897X

Aerial Vision-Dialog Navigation (AVDN) is a new task that requires drones to navigate to a target location based on human-robot dialog history. This paper focuses on the critical fine-grained cross-modal alignment problem in AVDN, requiring the drone to align language entities with visual landmarks in top-down views. To achieve this, we first construct a Fine-Grained AVDN (FG-AVDN) dataset via a semi-automatic annotation pipeline, providing diverse multimodal annotations at the entity-landmark level. Based on this, a novel Fine-grained Entity-Landmark Alignment (FELA) method is proposed to learn the cross-modal alignment explicitly. Concretely, FELA first boosts the drone’s visual understanding with a precise semantic grid representation, which captures the environmental semantics and spatial structure simultaneously. Subsequently, to learn the entity-landmark alignment, we devise cross-modal auxiliary tasks from three perspectives, including grounding, captioning, and contrastive learning. Extensive experiments demonstrate that our explicit entity-landmark alignment learning is beneficial for AVDN. As a result, FELA achieves leading performance with 3.2% SR and 4.9% GP improvements over prior arts. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Robots

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